{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Joins & Union Tutorial for Beginners "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial Overview\n",
    "\n",
    "### Part 1\n",
    "1. Loading CSV Data into Pandas df\n",
    "2. Left Join\n",
    "\n",
    "### Part 2\n",
    "3. Inner Join\n",
    "4. Full Join\n",
    "5. Cross Join\n",
    "6. Right Join - Why bother?\n",
    "7. Union All / Concat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing / Installing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Packages / libraries\n",
    "import os #provides functions for interacting with the operating system\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# To install Pandas type \"pip install pandas\" to the anaconda terminal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading CSV Data into Pandas df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\pitsi\\\\Desktop\\\\Python Tutorials'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is to find out your current directory\n",
    "cwd = os.getcwd()\n",
    "cwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(124, 3)\n",
      "(119, 4)\n",
      "(31, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08/03/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1681.714286</td>\n",
       "      <td>13112.47619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/03/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1690.298030</td>\n",
       "      <td>13201.38342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10/03/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1698.881773</td>\n",
       "      <td>13290.29064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/03/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1707.465517</td>\n",
       "      <td>13379.19787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12/03/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1716.049261</td>\n",
       "      <td>13468.10509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name     Visitors      Revenue\n",
       "0  08/03/2021     Monday  1681.714286  13112.47619\n",
       "1  09/03/2021    Tuesday  1690.298030  13201.38342\n",
       "2  10/03/2021  Wednesday  1698.881773  13290.29064\n",
       "3  11/03/2021   Thursday  1707.465517  13379.19787\n",
       "4  12/03/2021     Friday  1716.049261  13468.10509"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data\n",
    "marketing_raw = pd.read_csv('F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\Marketing Raw Data.csv')\n",
    "revenue_raw = pd.read_csv('F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\Revenue Raw Data.csv')\n",
    "revenue_raw_new = pd.read_csv('F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\Revenue Raw Data (new).csv')\n",
    "\n",
    "\n",
    "# Shapes\n",
    "print(marketing_raw.shape)\n",
    "print(revenue_raw.shape)\n",
    "print(revenue_raw_new.shape)\n",
    "\n",
    "#Visualise the data\n",
    "\n",
    "marketing_raw.head()\n",
    "revenue_raw.head()\n",
    "revenue_raw_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Left Join\n",
    "\n",
    "When you are joining 2+ tables together "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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etDE3fR/oT+P7TbKJ5+9rJfeYxsf+mrvn1d+Tkm/sFe5fmXRWyHMyrnJfjjgs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0P5ufnv6ZEsHY9LHJfzyZgOFw5ktyCIL1KhdGBNT0o1edhJYeqCPJ1H+fy1yhGaLlPRwkWjLetw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\nYPKP0jTPBRe58jH6+x0doy5a9Pfkz3uV+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f9g2o7p9c/Pm45JUJa9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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 9000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual representation of Left Join\n",
    "\n",
    "import os\n",
    "from IPython.display import Image\n",
    "PATH = \"F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\\"\n",
    "Image(filename = PATH + \"Left Join.png\", width=9000, height=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows for Table A is: (119, 4)\n",
      "The number of rows for Table B is: (124, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue\n",
       "0  09/11/2020     Monday       707     5211\n",
       "1  10/11/2020    Tuesday      1455    10386\n",
       "2  11/11/2020  Wednesday      1520    12475\n",
       "3  12/11/2020   Thursday      1726    14414\n",
       "4  13/11/2020     Friday      2134    20916"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The number of rows for Table A is:', revenue_raw.shape)\n",
    "\n",
    "print('The number of rows for Table B is:', marketing_raw.shape)\n",
    "\n",
    "revenue_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22/12/2020</td>\n",
       "      <td>1024.500000</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/12/2020</td>\n",
       "      <td>1181.700000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/12/2020</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25/12/2020</td>\n",
       "      <td>2336.777778</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26/12/2020</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Marketing Spend           Promo\n",
       "0  22/12/2020      1024.500000   Promotion Red\n",
       "1  23/12/2020      1181.700000  Promotion Blue\n",
       "2  24/12/2020      1955.000000        No Promo\n",
       "3  25/12/2020      2336.777778  Promotion Blue\n",
       "4  26/12/2020      4535.375000  Promotion Blue"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marketing_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue Promo\n",
       "0  09/11/2020     Monday       707     5211   NaN\n",
       "1  10/11/2020    Tuesday      1455    10386   NaN\n",
       "2  11/11/2020  Wednesday      1520    12475   NaN\n",
       "3  12/11/2020   Thursday      1726    14414   NaN\n",
       "4  13/11/2020     Friday      2134    20916   NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example 1 - Both tables in Brackets\n",
    "df = pd.merge(revenue_raw, marketing_raw, how = 'left', left_on = ['Date'], right_on = ['Date'])\n",
    "df.shape\n",
    "\n",
    "# Example 2 - One table in Brackets\n",
    "df2 = revenue_raw.merge(marketing_raw, how = 'left', left_on = ['Date'], right_on = ['Date'])\n",
    "df2\n",
    "\n",
    "# Example 3 - Filtering specific columns\n",
    "df3 = revenue_raw.merge(marketing_raw[['Date','Promo']], how = 'left', left_on = ['Date'], right_on = ['Date'])\n",
    "df3\n",
    "\n",
    "# Example 4 - Same name key\n",
    "\n",
    "df4 = revenue_raw.merge(marketing_raw[['Date','Promo']], how = 'left', on = ['Date'])\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_ID</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22/12/2020</td>\n",
       "      <td>1024.500000</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/12/2020</td>\n",
       "      <td>1181.700000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/12/2020</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25/12/2020</td>\n",
       "      <td>2336.777778</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26/12/2020</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date_ID  Marketing Spend           Promo\n",
       "0  22/12/2020      1024.500000   Promotion Red\n",
       "1  23/12/2020      1181.700000  Promotion Blue\n",
       "2  24/12/2020      1955.000000        No Promo\n",
       "3  25/12/2020      2336.777778  Promotion Blue\n",
       "4  26/12/2020      4535.375000  Promotion Blue"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example 5 - Renaming the column names to demonstrate the left_on and right_on\n",
    "marketing_raw.columns = marketing_raw.columns.str.replace('Date', 'Date_ID')\n",
    "marketing_raw.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Run the error\n",
    "# Example 4 - Same name key\n",
    "\n",
    "df4 = revenue_raw.merge(marketing_raw[['Date_ID','Promo']], how = 'left', left_on = ['Date'], right_on = ['Date_ID'])\n",
    "df4.shape\n",
    "\n",
    "# Run the correct code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend Promo\n",
       "0  09/11/2020     Monday       707     5211              NaN   NaN\n",
       "1  10/11/2020    Tuesday      1455    10386              NaN   NaN\n",
       "2  11/11/2020  Wednesday      1520    12475              NaN   NaN\n",
       "3  12/11/2020   Thursday      1726    14414              NaN   NaN\n",
       "4  13/11/2020     Friday      2134    20916              NaN   NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend     Promo\n",
       "0  09/11/2020     Monday       707     5211              0.0  No Promo\n",
       "1  10/11/2020    Tuesday      1455    10386              0.0  No Promo\n",
       "2  11/11/2020  Wednesday      1520    12475              0.0  No Promo\n",
       "3  12/11/2020   Thursday      1726    14414              0.0  No Promo\n",
       "4  13/11/2020     Friday      2134    20916              0.0  No Promo"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the NaN with 0\n",
    "df.fillna(0)\n",
    "\n",
    "df[['Marketing Spend']] = df[['Marketing Spend']].fillna(0)\n",
    "df\n",
    "\n",
    "# Replacing the NaN with a string\n",
    "df[['Promo']] = df[['Promo']].fillna('No Promo2')\n",
    "df\n",
    "\n",
    "# Replacing strings with strings\n",
    "df[['Promo']] = df[['Promo']].replace('No Promo2','No Promo')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Inner Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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IVr8P9Lfp/SZZkPP3tbJ7TPG2P3H3vMZ7UvIkXeP+lUlng2NW4y735YjTQuoS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\n1Bsk2TEU2f7b5WNOeWj17o/5UTy2Tfft40PN856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h2la077r0TnGBaxPbR+M+YOsqzpd5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Yp639ur1DuuaO3Tgkv5Y8gxl6R3jfZ3xDztiGNcBeeGCeM+zveM9e0XE+Xc4n/RpbaYKIFrev7x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\nN83H53shjYl8nnMX3uMx3h5H3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rQvzfZeed+Gf8fbM9lHAD4yBUOA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\nAABgxxQMAQAAAAAAYLfO5/8HI5plqVz4eDAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 9000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual representation of Left Join\n",
    "\n",
    "import os\n",
    "from IPython.display import Image\n",
    "PATH = \"F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\\"\n",
    "Image(filename = PATH + \"Inner Join.png\", width=9000, height=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows for Table A is: (119, 4)\n",
      "The number of rows for Table B is: (124, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue\n",
       "0  09/11/2020     Monday       707     5211\n",
       "1  10/11/2020    Tuesday      1455    10386\n",
       "2  11/11/2020  Wednesday      1520    12475\n",
       "3  12/11/2020   Thursday      1726    14414\n",
       "4  13/11/2020     Friday      2134    20916"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The number of rows for Table A is:', revenue_raw.shape)\n",
    "\n",
    "print('The number of rows for Table B is:', marketing_raw.shape)\n",
    "\n",
    "revenue_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_ID</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22/12/2020</td>\n",
       "      <td>1024.500000</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/12/2020</td>\n",
       "      <td>1181.700000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/12/2020</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25/12/2020</td>\n",
       "      <td>2336.777778</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26/12/2020</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date_ID  Marketing Spend           Promo\n",
       "0  22/12/2020      1024.500000   Promotion Red\n",
       "1  23/12/2020      1181.700000  Promotion Blue\n",
       "2  24/12/2020      1955.000000        No Promo\n",
       "3  25/12/2020      2336.777778  Promotion Blue\n",
       "4  26/12/2020      4535.375000  Promotion Blue"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marketing_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22/12/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1571</td>\n",
       "      <td>10245</td>\n",
       "      <td>1024.500000</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/12/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1288</td>\n",
       "      <td>11817</td>\n",
       "      <td>1181.700000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/12/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2556</td>\n",
       "      <td>19550</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25/12/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2152</td>\n",
       "      <td>21031</td>\n",
       "      <td>2336.777778</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26/12/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678</td>\n",
       "      <td>36283</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "0  22/12/2020    Tuesday      1571    10245      1024.500000   Promotion Red\n",
       "1  23/12/2020  Wednesday      1288    11817      1181.700000  Promotion Blue\n",
       "2  24/12/2020   Thursday      2556    19550      1955.000000        No Promo\n",
       "3  25/12/2020     Friday      2152    21031      2336.777778  Promotion Blue\n",
       "4  26/12/2020   Saturday      2678    36283      4535.375000  Promotion Blue"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example 1 - Inner Join\n",
    "df = pd.merge(revenue_raw, marketing_raw, how = 'inner', left_on = ['Date'], right_on = ['Date'])\n",
    "df.shape\n",
    "\n",
    "\n",
    "# print the shapes\n",
    "print(df.shape)\n",
    "# print the output\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Full Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ChND5JP0dAGBL2AQdX4J2/f7VfaN79GduTib63bXE+g72hPHm3Lp3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DMbhvMiQT7v4LxoKlInU0jsW21+mbOeKWlvjjOcX3AOsGL7W0ChXSwl7by8L8ypcxV/D9G8YrHl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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 9000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual representation of Left Join\n",
    "\n",
    "import os\n",
    "from IPython.display import Image\n",
    "PATH = \"F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\\"\n",
    "Image(filename = PATH + \"Full Join.png\", width=9000, height=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows for Table A is: (119, 4)\n",
      "The number of rows for Table B is: (124, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue\n",
       "0  09/11/2020     Monday       707     5211\n",
       "1  10/11/2020    Tuesday      1455    10386\n",
       "2  11/11/2020  Wednesday      1520    12475\n",
       "3  12/11/2020   Thursday      1726    14414\n",
       "4  13/11/2020     Friday      2134    20916"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The number of rows for Table A is:', revenue_raw.shape)\n",
    "\n",
    "print('The number of rows for Table B is:', marketing_raw.shape)\n",
    "\n",
    "revenue_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22/12/2020</td>\n",
       "      <td>1024.500000</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/12/2020</td>\n",
       "      <td>1181.700000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/12/2020</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25/12/2020</td>\n",
       "      <td>2336.777778</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26/12/2020</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Marketing Spend           Promo\n",
       "0  22/12/2020      1024.500000   Promotion Red\n",
       "1  23/12/2020      1181.700000  Promotion Blue\n",
       "2  24/12/2020      1955.000000        No Promo\n",
       "3  25/12/2020      2336.777778  Promotion Blue\n",
       "4  26/12/2020      4535.375000  Promotion Blue"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marketing_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(175, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707.0</td>\n",
       "      <td>5211.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>10386.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>12475.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726.0</td>\n",
       "      <td>14414.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>20916.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend Promo\n",
       "0  09/11/2020     Monday     707.0   5211.0              NaN   NaN\n",
       "1  10/11/2020    Tuesday    1455.0  10386.0              NaN   NaN\n",
       "2  11/11/2020  Wednesday    1520.0  12475.0              NaN   NaN\n",
       "3  12/11/2020   Thursday    1726.0  14414.0              NaN   NaN\n",
       "4  13/11/2020     Friday    2134.0  20916.0              NaN   NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example 1 - Full join\n",
    "df = pd.merge(revenue_raw, marketing_raw, how = 'outer', left_on = ['Date'], right_on = ['Date'])\n",
    "\n",
    "\n",
    "# printing the shapes\n",
    "print(df.shape)\n",
    "\n",
    "# prininting the output\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[left_only, both, right_only]\n",
      "Categories (3, object): [left_only, both, right_only]\n",
      "A:  (51, 7)\n",
      "B:  (56, 7)\n",
      "Both:  (68, 7)\n",
      "175\n",
      "(175, 7)\n"
     ]
    }
   ],
   "source": [
    "# Example 2 - investigating which data comes from where\n",
    "# indicator will produce another column\n",
    "\n",
    "# full join with indicator\n",
    "df = pd.merge(revenue_raw, marketing_raw, how = 'outer', left_on = ['Date'], right_on = ['Date'], indicator = True)\n",
    "\n",
    "\n",
    "#printing the unique values of the new column\n",
    "print(df['_merge'].unique())\n",
    "df.head()\n",
    "\n",
    "# printing the shapes\n",
    "print('A: ', df[df['_merge'] == 'left_only'].shape)\n",
    "print('B: ', df[df['_merge'] == 'right_only'].shape)\n",
    "print('Both: ', df[df['_merge'] == 'both'].shape)\n",
    "\n",
    "# Doing the Math manually\n",
    "math = 51 + 56 + 68\n",
    "\n",
    "\n",
    "# printing the math\n",
    "print(math)\n",
    "print(df.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   # Please Like and Subscribe!!! Thank you!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Cross Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NPcgVBuvcfqHkuAcs3lUhknKdx5M/kHsDCRJ+tm/xxfzztKSWmRem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\nqhXNu6K5ERp72XfVKAnOz6Y9SFch7yL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iiG7tXEc7FFaL3Wbn1qxPMTFzft3+YUBmFMps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\n1/Pe/rlp1kH6y0j6uek+Y47V1eLo2lfrnVMPwDXScn+W8kyXb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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 9000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual representation of Left Join\n",
    "\n",
    "import os\n",
    "from IPython.display import Image\n",
    "PATH = \"F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\\"\n",
    "Image(filename = PATH + \"Cross Join.png\", width=9000, height=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# No pre-existing funtion I am aware for Cross Join - Hence we need to create a Function manually\n",
    "\n",
    "def cross_join(table1,table2):\n",
    "    return(table1.assign(key=1).merge(table2.assign(key=1), on='key').drop('key',1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows for Table A is: (119, 4)\n",
      "The number of rows for Table B is: (124, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue\n",
       "0  09/11/2020     Monday       707     5211\n",
       "1  10/11/2020    Tuesday      1455    10386\n",
       "2  11/11/2020  Wednesday      1520    12475\n",
       "3  12/11/2020   Thursday      1726    14414\n",
       "4  13/11/2020     Friday      2134    20916"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The number of rows for Table A is:', revenue_raw.shape)\n",
    "\n",
    "print('The number of rows for Table B is:', marketing_raw.shape)\n",
    "\n",
    "revenue_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22/12/2020</td>\n",
       "      <td>1024.500000</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23/12/2020</td>\n",
       "      <td>1181.700000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24/12/2020</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25/12/2020</td>\n",
       "      <td>2336.777778</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26/12/2020</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Marketing Spend           Promo\n",
       "0  22/12/2020      1024.500000   Promotion Red\n",
       "1  23/12/2020      1181.700000  Promotion Blue\n",
       "2  24/12/2020      1955.000000        No Promo\n",
       "3  25/12/2020      2336.777778  Promotion Blue\n",
       "4  26/12/2020      4535.375000  Promotion Blue"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marketing_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14756, 7)\n",
      "14756\n"
     ]
    }
   ],
   "source": [
    "# Run the Cross Join\n",
    "df = cross_join(revenue_raw, marketing_raw)\n",
    "df\n",
    "# PRint the shapes\n",
    "print(df.shape)\n",
    "\n",
    "# Do the math manually\n",
    "maths = 119 * 124\n",
    "\n",
    "# print the math\n",
    "print(maths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Right Join - Why bother?\n",
    "\n",
    "Never ever used it in real life - It is not needed as you can use a Left Join instead\n",
    "\n",
    "Works like a Left Join but from the right side"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Union All / Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D9miS/tub/WsAAKjAbDidDb011fRNZWpjnDdBauowm+ic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\nB8ZLHPdgT92vI8wcNUnOZ85a/d///V/9UtxbSAjZ7981OZN+b4f3a3InO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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 9000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual representation of Left Join\n",
    "\n",
    "import os\n",
    "from IPython.display import Image\n",
    "PATH = \"F:\\\\Github\\\\Python tutorials\\\\Python Joins & Unions\\\\\"\n",
    "Image(filename = PATH + \"Union All - Concat.png\", width=9000, height=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: (119, 4)\n",
      "B: (31, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>5211.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455.000000</td>\n",
       "      <td>10386.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520.000000</td>\n",
       "      <td>12475.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726.000000</td>\n",
       "      <td>14414.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134.000000</td>\n",
       "      <td>20916.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14/11/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1316.000000</td>\n",
       "      <td>12996.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15/11/2020</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>11929.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>10072.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1448.000000</td>\n",
       "      <td>12016.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1362.000000</td>\n",
       "      <td>9067.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2321.000000</td>\n",
       "      <td>17660.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1819.000000</td>\n",
       "      <td>15188.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21/11/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1180.000000</td>\n",
       "      <td>7813.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22/11/2020</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2138.000000</td>\n",
       "      <td>21963.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632.000000</td>\n",
       "      <td>34278.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1752.000000</td>\n",
       "      <td>18650.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>5574.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>26/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2105.000000</td>\n",
       "      <td>17294.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1671.000000</td>\n",
       "      <td>14760.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28/11/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1105.000000</td>\n",
       "      <td>8091.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>29/11/2020</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1698.000000</td>\n",
       "      <td>10647.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1541.000000</td>\n",
       "      <td>9144.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>01/12/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1245.000000</td>\n",
       "      <td>8307.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>02/12/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>14254.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>03/12/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2171.000000</td>\n",
       "      <td>16644.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>04/12/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>20607.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>05/12/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1186.000000</td>\n",
       "      <td>7765.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>06/12/2020</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>10075.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>07/12/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1584.000000</td>\n",
       "      <td>9612.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>08/12/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1961.000000</td>\n",
       "      <td>11601.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>09/12/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2464.000000</td>\n",
       "      <td>16300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10/12/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2394.000000</td>\n",
       "      <td>17740.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>11/12/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1322.000000</td>\n",
       "      <td>10140.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12/12/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1167.000000</td>\n",
       "      <td>9097.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>13/12/2020</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1163.000000</td>\n",
       "      <td>8802.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>14/12/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1772.000000</td>\n",
       "      <td>11679.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>15/12/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1740.000000</td>\n",
       "      <td>11952.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>16/12/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1317.000000</td>\n",
       "      <td>9213.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>17/12/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2330.000000</td>\n",
       "      <td>17618.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>18/12/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2056.000000</td>\n",
       "      <td>18161.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>19/12/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>8800.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20/12/2020</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1828.000000</td>\n",
       "      <td>11737.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>21/12/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1605.000000</td>\n",
       "      <td>12038.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22/12/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1571.000000</td>\n",
       "      <td>10245.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>23/12/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1288.000000</td>\n",
       "      <td>11817.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>24/12/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2556.000000</td>\n",
       "      <td>19550.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>25/12/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2152.000000</td>\n",
       "      <td>21031.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>26/12/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678.000000</td>\n",
       "      <td>36283.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>27/12/2020</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>23014.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>28/12/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>8611.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>29/12/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1226.000000</td>\n",
       "      <td>7793.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>30/12/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1328.000000</td>\n",
       "      <td>9180.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>31/12/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2295.000000</td>\n",
       "      <td>19280.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>01/01/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2032.000000</td>\n",
       "      <td>21428.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>02/01/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>9062.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>03/01/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1442.000000</td>\n",
       "      <td>9952.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>04/01/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1476.000000</td>\n",
       "      <td>8563.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>05/01/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1272.000000</td>\n",
       "      <td>9418.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>06/01/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2054.000000</td>\n",
       "      <td>25766.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>07/01/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4139.000000</td>\n",
       "      <td>30146.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>08/01/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2286.000000</td>\n",
       "      <td>15962.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>09/01/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1371.000000</td>\n",
       "      <td>11249.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>10/01/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1484.000000</td>\n",
       "      <td>12129.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>11/01/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1571.000000</td>\n",
       "      <td>7300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>12/01/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1454.000000</td>\n",
       "      <td>8702.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>13/01/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1243.000000</td>\n",
       "      <td>7509.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>14/01/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2753.000000</td>\n",
       "      <td>22797.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>15/01/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2312.000000</td>\n",
       "      <td>21245.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>16/01/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1414.000000</td>\n",
       "      <td>11699.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>17/01/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1524.000000</td>\n",
       "      <td>9602.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>18/01/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1469.000000</td>\n",
       "      <td>8633.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>19/01/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1302.000000</td>\n",
       "      <td>7287.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>20/01/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1659.000000</td>\n",
       "      <td>9261.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>21/01/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2120.000000</td>\n",
       "      <td>14770.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>22/01/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2482.000000</td>\n",
       "      <td>24417.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>23/01/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1820.000000</td>\n",
       "      <td>11329.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>24/01/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>12445.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>25/01/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1430.000000</td>\n",
       "      <td>10925.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>26/01/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>488.000000</td>\n",
       "      <td>2898.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>27/01/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>7049.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>28/01/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1864.000000</td>\n",
       "      <td>12832.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>29/01/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1844.000000</td>\n",
       "      <td>13024.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>30/01/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1065.000000</td>\n",
       "      <td>7074.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>31/01/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1582.000000</td>\n",
       "      <td>9316.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>01/02/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1407.000000</td>\n",
       "      <td>10479.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>02/02/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1496.000000</td>\n",
       "      <td>12783.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>03/02/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2176.000000</td>\n",
       "      <td>18678.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>04/02/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>14696.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>05/02/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>17661.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>06/02/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>9095.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>07/02/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1608.000000</td>\n",
       "      <td>9199.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>08/02/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1337.000000</td>\n",
       "      <td>9294.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>09/02/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2266.000000</td>\n",
       "      <td>20860.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>10/02/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1925.000000</td>\n",
       "      <td>13602.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>11/02/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>826.000000</td>\n",
       "      <td>6546.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>12/02/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1387.000000</td>\n",
       "      <td>10310.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13/02/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>3392.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>14/02/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>968.000000</td>\n",
       "      <td>5772.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>15/02/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1482.000000</td>\n",
       "      <td>9987.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>16/02/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1420.000000</td>\n",
       "      <td>8422.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>17/02/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1171.000000</td>\n",
       "      <td>7809.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>18/02/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2395.000000</td>\n",
       "      <td>24247.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>19/02/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2029.000000</td>\n",
       "      <td>14706.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>20/02/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1047.000000</td>\n",
       "      <td>6427.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>21/02/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1464.000000</td>\n",
       "      <td>7462.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>22/02/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>8371.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>23/02/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1154.000000</td>\n",
       "      <td>9135.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>24/02/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1841.000000</td>\n",
       "      <td>13719.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>25/02/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2248.000000</td>\n",
       "      <td>20237.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>26/02/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2553.000000</td>\n",
       "      <td>26608.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>27/02/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>12789.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>28/02/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1912.000000</td>\n",
       "      <td>12036.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>01/03/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1786.000000</td>\n",
       "      <td>13212.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>02/03/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>6155.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>03/03/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1068.000000</td>\n",
       "      <td>8223.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>04/03/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>16727.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>05/03/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1806.000000</td>\n",
       "      <td>15474.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>06/03/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1621.000000</td>\n",
       "      <td>12850.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>07/03/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>6681.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>08/03/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1681.714286</td>\n",
       "      <td>13112.47619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>09/03/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1690.298030</td>\n",
       "      <td>13201.38342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>10/03/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1698.881773</td>\n",
       "      <td>13290.29064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>11/03/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1707.465517</td>\n",
       "      <td>13379.19787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>12/03/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1716.049261</td>\n",
       "      <td>13468.10509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>13/03/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1724.633005</td>\n",
       "      <td>13557.01232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>14/03/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1733.216749</td>\n",
       "      <td>13645.91954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>15/03/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1741.800493</td>\n",
       "      <td>13734.82677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>16/03/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1750.384236</td>\n",
       "      <td>13823.73399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>17/03/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1758.967980</td>\n",
       "      <td>13912.64122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>18/03/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1767.551724</td>\n",
       "      <td>14001.54844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>19/03/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1776.135468</td>\n",
       "      <td>14090.45567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>20/03/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1784.719212</td>\n",
       "      <td>14179.36289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>21/03/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1793.302956</td>\n",
       "      <td>14268.27011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>22/03/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1801.886700</td>\n",
       "      <td>14357.17734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>23/03/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1810.470443</td>\n",
       "      <td>14446.08456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>24/03/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1819.054187</td>\n",
       "      <td>14534.99179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>25/03/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1827.637931</td>\n",
       "      <td>14623.89901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>26/03/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1836.221675</td>\n",
       "      <td>14712.80624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>27/03/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1844.805419</td>\n",
       "      <td>14801.71346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>28/03/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1853.389163</td>\n",
       "      <td>14890.62069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>29/03/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1861.972906</td>\n",
       "      <td>14979.52791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>30/03/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1870.556650</td>\n",
       "      <td>15068.43514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>31/03/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1879.140394</td>\n",
       "      <td>15157.34236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>01/04/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1887.724138</td>\n",
       "      <td>15246.24959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>02/04/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1896.307882</td>\n",
       "      <td>15335.15681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>03/04/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1904.891626</td>\n",
       "      <td>15424.06404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>04/04/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1913.475369</td>\n",
       "      <td>15512.97126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>05/04/2021</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1922.059113</td>\n",
       "      <td>15601.87849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>06/04/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1930.642857</td>\n",
       "      <td>15690.78571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>07/04/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1939.226601</td>\n",
       "      <td>15779.69294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Day_Name     Visitors      Revenue\n",
       "0    09/11/2020     Monday   707.000000   5211.00000\n",
       "1    10/11/2020    Tuesday  1455.000000  10386.00000\n",
       "2    11/11/2020  Wednesday  1520.000000  12475.00000\n",
       "3    12/11/2020   Thursday  1726.000000  14414.00000\n",
       "4    13/11/2020     Friday  2134.000000  20916.00000\n",
       "5    14/11/2020   Saturday  1316.000000  12996.00000\n",
       "6    15/11/2020     Sunday  1287.000000  11929.00000\n",
       "7    16/11/2020     Monday  1548.000000  10072.00000\n",
       "8    17/11/2020    Tuesday  1448.000000  12016.00000\n",
       "9    18/11/2020  Wednesday  1362.000000   9067.00000\n",
       "10   19/11/2020   Thursday  2321.000000  17660.00000\n",
       "11   20/11/2020     Friday  1819.000000  15188.00000\n",
       "12   21/11/2020   Saturday  1180.000000   7813.00000\n",
       "13   22/11/2020     Sunday  2138.000000  21963.00000\n",
       "14   23/11/2020     Monday  2632.000000  34278.00000\n",
       "15   24/11/2020    Tuesday  1752.000000  18650.00000\n",
       "16   25/11/2020  Wednesday   627.000000   5574.00000\n",
       "17   26/11/2020   Thursday  2105.000000  17294.00000\n",
       "18   27/11/2020     Friday  1671.000000  14760.00000\n",
       "19   28/11/2020   Saturday  1105.000000   8091.00000\n",
       "20   29/11/2020     Sunday  1698.000000  10647.00000\n",
       "21   30/11/2020     Monday  1541.000000   9144.00000\n",
       "22   01/12/2020    Tuesday  1245.000000   8307.00000\n",
       "23   02/12/2020  Wednesday  1955.000000  14254.00000\n",
       "24   03/12/2020   Thursday  2171.000000  16644.00000\n",
       "25   04/12/2020     Friday  1975.000000  20607.00000\n",
       "26   05/12/2020   Saturday  1186.000000   7765.00000\n",
       "27   06/12/2020     Sunday  1822.000000  10075.00000\n",
       "28   07/12/2020     Monday  1584.000000   9612.00000\n",
       "29   08/12/2020    Tuesday  1961.000000  11601.00000\n",
       "30   09/12/2020  Wednesday  2464.000000  16300.00000\n",
       "31   10/12/2020   Thursday  2394.000000  17740.00000\n",
       "32   11/12/2020     Friday  1322.000000  10140.00000\n",
       "33   12/12/2020   Saturday  1167.000000   9097.00000\n",
       "34   13/12/2020     Sunday  1163.000000   8802.00000\n",
       "35   14/12/2020     Monday  1772.000000  11679.00000\n",
       "36   15/12/2020    Tuesday  1740.000000  11952.00000\n",
       "37   16/12/2020  Wednesday  1317.000000   9213.00000\n",
       "38   17/12/2020   Thursday  2330.000000  17618.00000\n",
       "39   18/12/2020     Friday  2056.000000  18161.00000\n",
       "40   19/12/2020   Saturday  1191.000000   8800.00000\n",
       "41   20/12/2020     Sunday  1828.000000  11737.00000\n",
       "42   21/12/2020     Monday  1605.000000  12038.00000\n",
       "43   22/12/2020    Tuesday  1571.000000  10245.00000\n",
       "44   23/12/2020  Wednesday  1288.000000  11817.00000\n",
       "45   24/12/2020   Thursday  2556.000000  19550.00000\n",
       "46   25/12/2020     Friday  2152.000000  21031.00000\n",
       "47   26/12/2020   Saturday  2678.000000  36283.00000\n",
       "48   27/12/2020     Sunday  2020.000000  23014.00000\n",
       "49   28/12/2020     Monday  1480.000000   8611.00000\n",
       "50   29/12/2020    Tuesday  1226.000000   7793.00000\n",
       "51   30/12/2020  Wednesday  1328.000000   9180.00000\n",
       "52   31/12/2020   Thursday  2295.000000  19280.00000\n",
       "53   01/01/2021     Friday  2032.000000  21428.00000\n",
       "54   02/01/2021   Saturday  1030.000000   9062.00000\n",
       "55   03/01/2021     Sunday  1442.000000   9952.00000\n",
       "56   04/01/2021     Monday  1476.000000   8563.00000\n",
       "57   05/01/2021    Tuesday  1272.000000   9418.00000\n",
       "58   06/01/2021  Wednesday  2054.000000  25766.00000\n",
       "59   07/01/2021   Thursday  4139.000000  30146.00000\n",
       "60   08/01/2021     Friday  2286.000000  15962.00000\n",
       "61   09/01/2021   Saturday  1371.000000  11249.00000\n",
       "62   10/01/2021     Sunday  1484.000000  12129.00000\n",
       "63   11/01/2021     Monday  1571.000000   7300.00000\n",
       "64   12/01/2021    Tuesday  1454.000000   8702.00000\n",
       "65   13/01/2021  Wednesday  1243.000000   7509.00000\n",
       "66   14/01/2021   Thursday  2753.000000  22797.00000\n",
       "67   15/01/2021     Friday  2312.000000  21245.00000\n",
       "68   16/01/2021   Saturday  1414.000000  11699.00000\n",
       "69   17/01/2021     Sunday  1524.000000   9602.00000\n",
       "70   18/01/2021     Monday  1469.000000   8633.00000\n",
       "71   19/01/2021    Tuesday  1302.000000   7287.00000\n",
       "72   20/01/2021  Wednesday  1659.000000   9261.00000\n",
       "73   21/01/2021   Thursday  2120.000000  14770.00000\n",
       "74   22/01/2021     Friday  2482.000000  24417.00000\n",
       "75   23/01/2021   Saturday  1820.000000  11329.00000\n",
       "76   24/01/2021     Sunday  1628.000000  12445.00000\n",
       "77   25/01/2021     Monday  1430.000000  10925.00000\n",
       "78   26/01/2021    Tuesday   488.000000   2898.00000\n",
       "79   27/01/2021  Wednesday  1084.000000   7049.00000\n",
       "80   28/01/2021   Thursday  1864.000000  12832.00000\n",
       "81   29/01/2021     Friday  1844.000000  13024.00000\n",
       "82   30/01/2021   Saturday  1065.000000   7074.00000\n",
       "83   31/01/2021     Sunday  1582.000000   9316.00000\n",
       "84   01/02/2021     Monday  1407.000000  10479.00000\n",
       "85   02/02/2021    Tuesday  1496.000000  12783.00000\n",
       "86   03/02/2021  Wednesday  2176.000000  18678.00000\n",
       "87   04/02/2021   Thursday  2010.000000  14696.00000\n",
       "88   05/02/2021     Friday  2015.000000  17661.00000\n",
       "89   06/02/2021   Saturday  1488.000000   9095.00000\n",
       "90   07/02/2021     Sunday  1608.000000   9199.00000\n",
       "91   08/02/2021     Monday  1337.000000   9294.00000\n",
       "92   09/02/2021    Tuesday  2266.000000  20860.00000\n",
       "93   10/02/2021  Wednesday  1925.000000  13602.00000\n",
       "94   11/02/2021   Thursday   826.000000   6546.00000\n",
       "95   12/02/2021     Friday  1387.000000  10310.00000\n",
       "96   13/02/2021   Saturday   599.000000   3392.00000\n",
       "97   14/02/2021     Sunday   968.000000   5772.00000\n",
       "98   15/02/2021     Monday  1482.000000   9987.00000\n",
       "99   16/02/2021    Tuesday  1420.000000   8422.00000\n",
       "100  17/02/2021  Wednesday  1171.000000   7809.00000\n",
       "101  18/02/2021   Thursday  2395.000000  24247.00000\n",
       "102  19/02/2021     Friday  2029.000000  14706.00000\n",
       "103  20/02/2021   Saturday  1047.000000   6427.00000\n",
       "104  21/02/2021     Sunday  1464.000000   7462.00000\n",
       "105  22/02/2021     Monday  1428.000000   8371.00000\n",
       "106  23/02/2021    Tuesday  1154.000000   9135.00000\n",
       "107  24/02/2021  Wednesday  1841.000000  13719.00000\n",
       "108  25/02/2021   Thursday  2248.000000  20237.00000\n",
       "109  26/02/2021     Friday  2553.000000  26608.00000\n",
       "110  27/02/2021   Saturday  1797.000000  12789.00000\n",
       "111  28/02/2021     Sunday  1912.000000  12036.00000\n",
       "112  01/03/2021     Monday  1786.000000  13212.00000\n",
       "113  02/03/2021    Tuesday  1096.000000   6155.00000\n",
       "114  03/03/2021  Wednesday  1068.000000   8223.00000\n",
       "115  04/03/2021   Thursday  1976.000000  16727.00000\n",
       "116  05/03/2021     Friday  1806.000000  15474.00000\n",
       "117  06/03/2021   Saturday  1621.000000  12850.00000\n",
       "118  07/03/2021     Sunday  1001.000000   6681.00000\n",
       "119  08/03/2021     Monday  1681.714286  13112.47619\n",
       "120  09/03/2021    Tuesday  1690.298030  13201.38342\n",
       "121  10/03/2021  Wednesday  1698.881773  13290.29064\n",
       "122  11/03/2021   Thursday  1707.465517  13379.19787\n",
       "123  12/03/2021     Friday  1716.049261  13468.10509\n",
       "124  13/03/2021   Saturday  1724.633005  13557.01232\n",
       "125  14/03/2021     Sunday  1733.216749  13645.91954\n",
       "126  15/03/2021     Monday  1741.800493  13734.82677\n",
       "127  16/03/2021    Tuesday  1750.384236  13823.73399\n",
       "128  17/03/2021  Wednesday  1758.967980  13912.64122\n",
       "129  18/03/2021   Thursday  1767.551724  14001.54844\n",
       "130  19/03/2021     Friday  1776.135468  14090.45567\n",
       "131  20/03/2021   Saturday  1784.719212  14179.36289\n",
       "132  21/03/2021     Sunday  1793.302956  14268.27011\n",
       "133  22/03/2021     Monday  1801.886700  14357.17734\n",
       "134  23/03/2021    Tuesday  1810.470443  14446.08456\n",
       "135  24/03/2021  Wednesday  1819.054187  14534.99179\n",
       "136  25/03/2021   Thursday  1827.637931  14623.89901\n",
       "137  26/03/2021     Friday  1836.221675  14712.80624\n",
       "138  27/03/2021   Saturday  1844.805419  14801.71346\n",
       "139  28/03/2021     Sunday  1853.389163  14890.62069\n",
       "140  29/03/2021     Monday  1861.972906  14979.52791\n",
       "141  30/03/2021    Tuesday  1870.556650  15068.43514\n",
       "142  31/03/2021  Wednesday  1879.140394  15157.34236\n",
       "143  01/04/2021   Thursday  1887.724138  15246.24959\n",
       "144  02/04/2021     Friday  1896.307882  15335.15681\n",
       "145  03/04/2021   Saturday  1904.891626  15424.06404\n",
       "146  04/04/2021     Sunday  1913.475369  15512.97126\n",
       "147  05/04/2021     Monday  1922.059113  15601.87849\n",
       "148  06/04/2021    Tuesday  1930.642857  15690.78571\n",
       "149  07/04/2021  Wednesday  1939.226601  15779.69294"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setting the max rows\n",
    "pd.set_option('display.max_rows', 500)\n",
    "\n",
    "# printing the shapes\n",
    "print('A:', revenue_raw.shape )\n",
    "print('B:', revenue_raw_new.shape )\n",
    "\n",
    "# Union all / Concat - Example 1\n",
    "df = pd.concat([revenue_raw, revenue_raw_new], ignore_index = True)\n",
    "df.shape\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
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       "      <td>20916</td>\n",
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       "         Date   Day_Name  Visitors  Revenue\n",
       "0  09/11/2020     Monday       707     5211\n",
       "1  10/11/2020    Tuesday      1455    10386\n",
       "2  11/11/2020  Wednesday      1520    12475\n",
       "3  12/11/2020   Thursday      1726    14414\n",
       "4  13/11/2020     Friday      2134    20916"
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     "execution_count": 82,
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   ],
   "source": [
    "revenue_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
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       "         Date   Day_Name     Visitors      Revenue\n",
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    "revenue_raw_new.head()"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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   ],
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